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Epicor 9 and Vantage AI Assistant Sizing and Value Calculator

This calculator sizes a private AI assistant over a legacy Epicor 9 or Vantage system, using a replicated reporting copy. It estimates GPUs, index size, hours saved and payback from inputs you set.

Your numbers

Named users

users

Users licensed to use the assistant.

Daily active share

45 %

Share of named users who ask at least one question on a given day.

Questions per active user per day

questions

Pilot logs give the real figure; 5 to 15 is common once habit forms.

Prompt plus answer tokens per question

tokens

Question, rows from the reporting copy and document chunks, plus the answer. Keep context short on small GPUs.

Documents to index

documents

Scanned prints, certs, work instructions and order documents on file shares.

Average pages per document

pages

Average across the document set.

Minutes saved per question

minutes

Legacy screens are slow to navigate, so saved time per question is often higher than on modern ERPs.

Loaded hourly cost

per hour

Salary plus benefits and overhead for the people asking.

GPU monthly cost

per GPU per month

Hardware amortised over 36 months plus power, cooling, support and rack space.

Throughput per GPU

tokens/s

Aggregate tokens per second under batching for your model and quantisation. Measure it; this is the most sensitive assumption.

One-time implementation cost

one-time

Integration, document pipeline, security review and pilot.

Your results

GPUs needed (before redundancy)

Peak-hour tokens per second divided by throughput per GPU, rounded up. Add a spare GPU if you need failover.

Monthly value

$16,207

Hours saved x loaded hourly cost. Treat as an upper bound until a pilot confirms the time saved.

Questions per day

158

Named users x active share x questions per active user.

Tokens per day

472,500

Questions per day x tokens per question.

Peak concurrent requests

Assumes 25 percent of daily questions land in the busiest hour and each request holds a slot for about 15 seconds.

Vector index size

2.4576

Gigabytes, assuming 2 chunks per page, 1024-dimension float32 embeddings and 50 percent index overhead. Original files are extra.

Hours saved per month

386 hrs

Questions per day x minutes saved, over 21 working days.

Simple payback

2.5 months

One-time cost divided by monthly value minus GPU cost. Shows 0 when value does not exceed GPU cost.

Planning estimate only. Tokens per second, time saved per question and peak share vary widely; replace the defaults with measurements from your own pilot before committing to hardware.

Save results:

GPUs needed vs questions per active user

Sweeps Questions per active user per day from 5 to 150, holding your other inputs fixed.

Epicor 9 and Vantage sizing review

Optional: send your numbers and get an engineer's second opinion on GPU count, index size and the value assumptions.

Optional. The tool and every export work without this. No spam.

How to use it

Enter named users, the daily active share, questions per user, and tokens per question. Legacy sites usually have fewer users and short context. Set the document count to what sits on file shares. Add your one-time cost, including replication setup, since that is a larger share of the total on a Progress system.

Include replication and data-dictionary work in one-time cost
Use a lower throughput if you plan a smaller model
Check whether saved time per question is realistic
Use the bar chart to find where a second GPU appears

The method

Questions per day come from users, active share and questions per user. Peak-hour tokens per second assume 25 percent of daily questions in the busiest hour. GPUs equal that rate divided by throughput per GPU. Index size multiplies documents, pages, chunks and float32 embeddings with 50 percent overhead. Value is hours saved x loaded cost over 21 working days.

Reading the result

At 70 users, one small GPU is usually enough, and the bar chart shows how far question volume can grow before a second is needed. If value is only slightly above GPU cost, the case depends on one-time cost staying low. A short payback does not remove the underlying risk of an unsupported platform.

Common mistakes

Common errors are sizing for peak benchmark throughput, forgetting that the replica and index need storage, and assuming the assistant makes figures correct when customisations alter them. Always show the query behind each answer.

Frequently Asked Questions

How many GPUs does a small Epicor 9 site need?

Usually one. With 70 named users and a few questions each per day, peak token load is modest, so a single 24 GB GPU with a 7B to 14B model typically covers it. Verify with measured throughput.

Why is the one-time cost higher on legacy systems?

Replication, driver setup, SQL width fixes and data-dictionary mapping take time on Progress databases. These costs are real, so enter them honestly; they also help later if you migrate.

Should I spend this instead of migrating?

They are different decisions. An assistant can reduce reporting backlog now and its assets help a migration later. If migration is already funded and imminent, align the two so effort is shared.

Is the value figure guaranteed?

No. It is an upper bound based on your minutes-saved input. Pilot with a small group and replace the estimate with measured data before using it in a budget request.

If you would like these numbers validated, or help deciding between an assistant and a migration, Netray can walk through it with you. The calculator needs no contact.

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